AI Agents in Practice — Read from the beginning

A practical, production-oriented guide to building AI agents — patterns over products, anti-hype, vendor-neutral. Part 1: The Demo Worked. Production Didn't. Priya's refund went through on a shipped order. The model was right. The system around it wasn't. Why agent demos break the moment they meet production — and what the demo hid that production reveals. Part 2: What Makes Something an Agent Define what an agent actually is in engineering terms — a control loop with tools, state, and boundaries. The three primitives an agent composes (MCP for acting, RAG for knowing, Skills for following reusable procedures). The bridge from manual ReAct to native tool calling. Part 3: How the Loop Actually Works Coming soon. What happens turn by turn when the agent runs. State that carries across turns, stopping conditions as real decisions, and context as a finite engineering resource — not just a bigger window. This series is actively maintained. New parts will be linked here as they publish. MCP in Practice — Read from the beginning The Model Context Protocol from first principles — what MCP is, why it exists, and how to build production-grade tool servers and clients. RAG in Practice — Read from the beginning Retrieval-augmented generation from first principles — why AI gets things wrong, what RAG fixes, and how the full pipeline works.